A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics

Home , Heterogeneous network, Vertical handover

applied sciences

Article A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics

Mykola Beshley 1 , Natalia Kryvinska 2,* , Oleg Yaremko 1 and Halyna Beshley 1

1 Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, 79013 Lviv, Ukraine; [email protected] (M.B.); [email protected] (O.Y.); [email protected] (H.B.) 2 Department of Information Systems, Faculty of Management, Comenius University, 25 82005 Bratislava, Slovakia * Correspondence: [email protected]

Abstract: With the heterogeneity and collaboration of many wireless operators (2G/3G/4G/5G/Wi-Fi), the priority is to effectively manage shared radio resources and ensure transparent user movement, which includes mechanisms such as mobility support, handover, quality of service (QoS), security and pricing. This requires considering the transition from the current mobile network architecture to a new paradigm based on collecting and storing information in big data for further analysis and decision making. For this reason, the management of big data analytics-driven networks in a cloud environment is an urgent issue, as the growth of its volume is becoming a challenge for today’s mobile infrastructure. Thus, we have formalized the problem of access network selection to improve the quality of mobile services through the efficient use of heterogeneous wireless network resources and Citation: Beshley, M.; Kryvinska, N.; optimal horizontal–vertical handover procedures. We proposed a method for adaptive selection of a Yaremko, O.; Beshley, H. A Self- wireless access node in a heterogeneous environment. A structural diagram of the optimization stages Optimizing Technique Based on for wireless heterogeneous networks was developed, making it possible to improve the efficiency of Vertical Handover for Load Balancing their functioning. A model for studying the processes of functioning of a heterogeneous network in Heterogeneous Wireless Networks environment is proposed. This model uses the methodology of big data evaluation to perform data Using Big Data Analytics. Appl. Sci. transmission monitoring, analysis of tasks generated by network users, and statistical output of 2021, 11, 4737. https://doi.org/ 10.3390/app11114737 vertical handover initiation in (2G/3G/4G/5G/Wi-Fi) mobile communication infrastructure. The model allows studying the issues of optimization of operators’ networks by implementing the Academic Editor: Murad Khan algorithm of redistribution of its network resources and providing flexible load balancing with QoS users in mind. The effectiveness of the proposed solutions is evaluated, and the performance of Received: 14 April 2021 the heterogeneous network is increased by 16% when using the method of static reservation of Accepted: 20 May 2021 network resources, compared to homogeneous networks, and another 13% when using a uniform Published: 21 May 2021 distribution of resources and a dynamic process of their reservation, as well as compared to the previous method. An appropriate self-optimizing technique based on vertical handover for load Publisher’s Note: MDPI stays neutral balancing in heterogeneous wireless networks, using big data analytics, improves the QoS for users. with regard to jurisdictional claims in published maps and institutional affil- Keywords: big data (BD); heterogeneous wireless networks (HWN); quality of service (QoS); self- iations. optimization; load balancing (LB); vertical handover (VHO); mobile network operators (MNOs)

Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. 1.1. Background and Problem Statement This article is an open access article distributed under the terms and Today, the volume of mobile traffic is growing rapidly due to the total spread of a conditions of the Creative Commons variety of mobile devices [1]. The main volume of network traffic is mobile video on the Attribution (CC BY) license (https:// internet, social media and popular services of the Internet of Things. Therefore, a solution creativecommons.org/licenses/by/ is needed that will enable the operator to move to a centralized and flexible heterogeneous 4.0/).

Appl. Sci. 2021, 11, 4737. https://doi.org/10.3390/app11114737 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 4737 2 of 24

network architecture in which resource management plays a crucial role, using the latest advances in information storage and cloud computing [2–4]. A large number of mobile devices are widely used and produce huge amounts of data every day [5]. This has a profound impact on society and social interaction and creates enormous challenges for Mobile Network Operators (MNOs) [6]. The volume, rate and variety of data from both mobile users and communication networks are increasing exponentially [7–10]. Accordingly, in the near future, there will be a need for data collection and analysis to make decisions about flexible resource management in heterogeneous systems. This will allow MNOs to analyze and predict the behavior and requirements of mobile users, which, in turn, will enable intelligent, real-time decision making across a wide range of applications [11,12]. By analyzing these data, mobile networks can actually provide and support a variety of intelligent services. New technologies are needed to process big data in a widescale, cost-effective and unstructured way [13]. Information about the unique characteristics of big data in mobile heterogeneous networks is an important element because it is critical to optimizing it [14]. Developing analytical methods will help MNOs to track and analyze different types of data, as well as resource status messages, across networks. Management logic and important statistics can be obtained from instantaneous data as well as from data in the history of statistics collected [15]. Useful information, such as the relationship between user behavior and network traffic, can help MNOs not only make decisions based on long-term strategies, but also optimize resource allocation to minimize allocations and operating costs [16]. An important challenge, however, is understanding the requirements of using big data (BD) analytics to deliver user services with quality of service and ensure highly efficient use of resources in future heterogeneous mobile networks.

1.2. Motivation The increasing range of services, infrastructure and trafﬁc volumes puts forward the scientiﬁc challenge of improving the performance of wireless heterogeneous network systems (2G/3G/4G/5G/Wi-Fi) and user quality of service by improving the method of adaptive radio access system selection and developing a collaborative resource management model, using big data technology.

1.3. Our Contributions The main novelty of our work is that we proposed a model for the study of the processes of functioning of a heterogeneous network environment, which, in contrast to the known, uses the methodology of processing big data to perform the monitoring of data transmissions, analysis of tasks that are formed by network users and statistical data output on the initiation of vertical handover in the (2G/3G/4G/5G/Wi-Fi) infrastructure of mobile communications, allowing to study the optimization of the operators’ network by implementing an algorithm for redistribution of its network resources and providing ﬂexible load balancing. Our contributions can be summarized as follows: • The problem of ensuring the effective functioning of a heterogeneous radio access network is formalized; • The method of increasing the efﬁciency of functioning of heterogeneous mobile communication networks based on big data technology is developed; • The realization of technologies for processing big data volumes, obtained by simulat- ing the process of functioning of a heterogeneous network is carried out; • The assessment of the effectiveness of the proposed solutions in relation to the optimization problem of the resources of a heterogeneous network of mobile communication is carried out. The remainder of this paper is organized as follows: Section2 presents the related works; Section3 presents the proposed solution in the paper, including the description of the self-optimizing technique based on vertical handover for load balancing in hetero- Appl. Sci. 2021, 11, 4737 3 of 24

geneous wireless networks, using big data analytics; Section4 presents the experimental results; and Section5 presents the conclusions of the study.

2. Related Work The classical vertical handover mechanism in heterogeneous network selection aims to choose the optimal network solution for the user; however, this may lead to partial networks accessing too many users, overloading the network and influencing the QoS of the customer. The network load-balancing approach presented in the paper [17] transforms the network-balancing problem into an optimization problem by constructing a network allocation matrix in the network that meets the user’s needs. The optimal allocation method is then obtained, using the optimization algorithm to effectively reach the balanced network utilization. In addition, this method is used to weight different networks to provide QoS requirements of different services. The modeling results demonstrate the effectiveness of the suggested algorithm. The proposed approach is a generic algorithm that can be applied to various heterogeneous networks, such as public wireless networks. Su et al. [18] adopted a comparison method that compares two parameters, namely, cell boundary crossings and handover execution, to optimize the overall network performance. The handover decision on a target cell is completely dependent on the signal strength measurement. Saeed et al. [19] developed a model to optimize the handover algorithm based on fuzzy logic for heterogenous network. In paper [20], the authors proposed a heterogeneous network handover based on a multiple attribute decision based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The method benefits in ordering by similarity to the ideal solution TOPSIS. Base stations are considered alternatives, and transmission metrics are treated as attributes for selecting the appropriate base station for transmission. In this paper, the authors proposed two modified TOPSIS methods for the purposes of transmission control in a heterogeneous network. The first method incorporates an entropy weighting technique for the transmission weighting metric. The second proposed method uses a standard deviation weighting technique to estimate the importance of each transmission metric. The simulation results show that the proposed methods outperform existing methods by reducing the number of frequent transmissions and radio failures, in addition to improving the average throughput of users. In current mobile networks, performance management (PM) is collected from network elements to a centralized system, the Network Management System (NMS), which acts as a business intelligence tool that specializes in monitoring and reporting on network performance. The performance management files include metrics and the named counters used to quantify network performance. Current NMS implementations have limitations in scalability and support for the volume, variety and speed of collected PM data, especially for 5G and 6G mobile network technologies. In order to overcome these limitations, the authors in [21] developed a big data framework based on an analysis of the following components: software architecture, data transmission, processing, reporting, and deployment. The authors also analyzed the PM file format on a real dataset from four different vendors and 2G, 3G, 4G, and 5G technologies. They then evaluated experimentally the appropriateness of the proposed framework, using a case study, including 5G PM files. The test results of the components and reports are presented, defining the hardware and software required to support up to one billion meters per hour. This suggestion can help telecom providers to adopt a big data reference system to address current and future calls on new mobile networks. The paper [22] explored a new fog computing system design to support device communication for (QoS) and Quality of Experience (QoE) enhancements. In particular, the authors focused on the potential of the fog computing orchestration system. How it can be adapted to next-generation cellular systems is an open task for research. The authors further proposed a mobility management procedure for fog networks, considering static and dynamic mobile nodes. As a result of the study, it was found that the proposed Appl. Sci. 2021, 11, 4737 4 of 24

work has the lowest power consumption, latency, latency, and signaling cost compared to LTE/LTE-A networks. In the paper [23], the authors identified the opportunities that 5G networks can provide and discussed the major challenges associated with implementing and achieving 5G goals. They also discussed recent advances in standardization, architectures that may be potential candidates for deployment, and energy challenges in 5G networks. Finally, the paper presented a big data perspective and the potential of machine learning for optimization and decision support in 5G networks. In the process of analysis of scientific works in the direction of development of heterogeneous mobile communication networks, it was found that the rapid growth of the volume of customer traffic in mobile networks and changes in its nature and structure require a continuous and significant increase in the capacity of these systems. Radio in- terface technologies practically reach the theoretical limits of channel bandwidth, so the further way to increase network capacity is spatial compaction and improvement of radio resource allocation management methods. To achieve greater network performance, it is proposed to use heterogeneous networks with cells of different sizes. A number of technical problems to be solved in heterogeneous networks are considered, namely, network planning, fighting inter-system interference, transport network organization, network management and its self-organization, mobility management, and the like. In addition, the strategic directions of software-defined networking/network functions virtualization (SDN/NFV) and cloud technologies are considered; the first is associated with increased network efficiency and service flexibility, while the second aims to take advantage of a combination of new business opportunities [24–28]. From the analysis of the work, it follows that the existing methods of improving the efficiency of mobile networks face the problem of a lack of technologies for managing heterogeneous networks, which would allow creating a flexible, manageable, adaptive and cost-effective system with prediction of the load from users and a focus on user satisfaction.

3. Proposed Solution 3.1. A Future Generation Heterogeneous Wireless Network Based on Big Data Analytics Today, progressive development in the field of telecommunications leads to the cre- ation of various radio access technologies and the emergence of a significant number of user devices supporting different mobile standards, which, in the near future, will combine different technologies into a single converged network and create a global heterogeneous mobile network [29]. This network will be formed of different segments of wireless technologies in which coverage areas are superimposed. This will increase the bandwidth of the network, expand its coverage area, and provide services to the user with better quality at no cost [30]. In heterogeneous, next-generation wireless networks, a user with a universal terminal will be able to access the networks of different telecommunications operators/providers. There is an urgent scientific and technical task for finding new methods for managing components of cellular networks and optimizing the parameters of fragments of cellular networks of different generations (2G–5G) and the subsystem of switching services in conditions of high user activity and the resulting congestion [31]. The mechanisms of the handover solution or the control of the switching between the communication channels can be centralized [the decision on the handover can be made in the user equipment (UE) itself (as in a mobile Wireless Local Area Network or in a network entity (e.g., cellular voice transmission))]. These cases are called Mobile-Controlled Handoff (MCHO) and Controlled-Network Handoff (NCHO) [32]. In the NCHO, the network makes a transmission decision based on the measurement of the RSS UE on a number of base stations. Signal quality information for all users is available at one point on the network, which facilitates the appropriate allocation of resources. This is advantageous when the handover decision is made by the network due to the following reasons: Appl. Sci. 2021, 11, 4737 5 of 24

• The network may redirect the UE to another network that has sufficient capacity to process its current communications; • The network can also coordinate the mobility of all UEs so that total traffic is evenly distributed across all resources, congestion is minimized, and total bandwidth is maximized. The disadvantage of NCHO is that the radio network may lack some parameters that affect transmission decisions, such as user requirements, the exact type of service, the number of active UEs, and some operator policies related to mobility between mobile Wi-Fi and 3GPP. Smart integration of Wi-Fi as part of the operator network provides significant benefits in terms of capacity and coverage, especially where people gather most often—modes of public transport, shopping malls, city centers, and more. Intelligent integration involves network selection and authentication of the operator who owns the Wi-Fi automatically and securely, while ensuring reliable and high-quality services. Integrated Wi-Fi networks will provide operators with more control and visibility when using Wi-Fi, as well as the ability to enforce common policies (as in 3G/4G networks). Operators are turning to the integration of Wi-Fi as an alternative radio access technology (RAT) to add capacity and to provide value-added services. There are several important benefits for a heterogeneous architecture over end-to-end QoS. In the MCHO, the UE can fully control the handover process. This type of handover has a short response time (about 0.1 s). The UE itself first detects all available networks. It then measures the signal levels from the surrounding base station (BS) and the interference levels on all channels and makes all the necessary estimates to address the handover. Transmission is initiated if the signal intensity of the serving BS is lower than that of another BS using a certain threshold. MCHO is the choice of the future as the telecommunications market migrates from a centralized operator approach to a customer-centric approach. The network-assisted handoff (NAHO) assists the UE in deciding to initiate handover by collecting and analyzing data. The UE may also provide its location and any other information that may be considered in the network analysis. The network only assists the UE in the adoption process, and the final decision will be made by the UE. As mentioned earlier, all information collected by the UE will be sent to the data center through the mobile operator. The data center is represented as big data, consisting of n servers. Big data opens the possibility of flexible interaction of terminal devices, both with the data center and with each other. This is achieved by the built-in drivers and system software of the cloud computing system. Physical switching will be performed by the UE, based on decision making by analyzing statistics in big data [33]. This will allow the monitoring of data transmission processes, the analyzing of any tasks, and, eventually, displaying the necessary types of reports on switching or initiating a handover. Accordingly, we proposed the architecture of the management system of the heterogeneous wireless network of the future generation in the development of 5G, which includes various radio access technologies with centralized management and the processing of large amounts of data. An important role in the flexibility of resource management of a heterogeneous network is played by peculiarities of the functioning of big data and collected statistical information from the characteristics of users and operators of the network. Thus, in the work, it is expedient to carry out research of these features of functioning with the help of technology big data, which are given in the following subsection. The proposed architecture of a future generation heterogeneous wireless network using big data for vertical handover management is depicted in Figure1. In a heterogeneous mobile system, data are conventionally divided into two types: user data, and network operator performance data. The analysis of both types of data can provide valuable information that can be used to optimize the network. Thus, mobile operators can analyze data to perform network planning, spectrum allocation, resource management, and the like. The data collected from UEs are very related to the user profile and behavior, including their location and mobility and personal data about the user’s QoS/QoE needs [34]. With Appl. Sci. 2021, 11, 4737 6 of 24

Appl. Sci. 2021, 11, x FOR PEER REVIEW 6 of 26

the rapid expansion of the mobile network and the development of smart mobile devices, an excessive amount of data are generated from the applications installed on UE users. The lected statistical information from the characteristics of users and operators of the net- data collected by the operators are mainly obtained from their database registers and RANs. work. Thus, in the work, it is expedient to carry out research of these features of function- Database registers have a large amount of service data regarding network performance, ing with the help of technology big data, which are given in the following subsection. successful call information and service usage priorities. Cloud Radio Access Network The proposed architecture of a future generation heterogeneous wireless network (C-RAN) is a novel mobile network architecture that is deemed to be one of the most using big data for vertical handover management is depicted in Figure 1. promising evolution trends for 5G networks [35].

FigureFigure 1. 1.ProposedProposed architecture architecture of ofa fu ature future generation generation heterogeneou heterogeneouss wireless wireless network network using using big big datadata for for vertical vertical handover handover management. management.

In a heterogeneous mobile system, data are conventionally divided into two types: user data, and network operator performance data. The analysis of both types of data can provide valuable information that can be used to optimize the network. Thus, mobile operators can analyze data to perform network planning, spectrum allocation, resource management, and the like. The data collected from UEs are very related to the user profile and

Appl. Sci. 2021, 11, 4737 7 of 24

3.2. Radio Access Network Selection Optimization in Heterogeneous Wireless Networks We formalized the problem of access network selection to improve the quality of mobile services through the efﬁcient use of heterogeneous wireless network resources and optimal load balancing vertical handover procedures. Let a closed space Ω be given in which a wireless heterogeneous network (or heterogeneous network consists of S radio access networks and a set of connections n = (1, 2, . . . , N), each consisting of individual wireless stations with different characteristics:

S = {Si}, i = 1, . . . , n (1)

where S is heterogeneous base stations or access points in a heterogeneous environment, and i is the number of stations. Ωp space of coverage by wireless stations, consists of the following:

p Ω = Ω1 ∪ Ω2 ∪ Ω3 ... ∪ Ωk, k = 1, . . . , m. (2)

Each wireless station Si ∈ S has a number of characteristics: χ(Si) = χi1, χi2,... χij,... χin , (3)

where χ(Si) is the characteristics of base stations or access points, and n is the total number of characteristics. In the space Ω under the inﬂuence of the set of wireless communication stations (CV) there exists a set of mobile devices functioning in roaming mode (without loading) or performing service operations. Let these mobile devices be as follows:

D = D1, D2,... D f ,..., Dm (4)

where m is the number of mobile devices, f is the position of the mobile device. Each mobile device Df ∈ D has a number of characteristics: n o η D f = η f 1, η f 2,... η f j,... η f n , (5)

where ηf is the characteristics of the mobile device Df.

n 0 F Ro S D f = S , S , S , (6)

0 F where S (Df) mobile device is not working (not enabled, faulty), S (Df) mobile device R is working in free mode (does not transmit data), and S (Df) mobile device is working (transmits data). Such information is necessary for the system of analysis of user activity in assessing the state of the network in the process of load balancing. Let us introduce a set of information processes (BP) to be executed in Ω space or to maintain their execution BP = {BPi : i = 1, . . . , L}, for example, to provide different information services (video, call, conference, cloud services). Consider the case of a single service process. Then, a single process BP consists of the following work operations: Ro = Ro1, Ro2,... Roj,... Rok , (7)

where k is the number of work operations (the value of this variable is deﬁned by the task setter); Roi is a work operation, included in the process. A work operation is an action that must be performed by a network node (server, router, switch) or a mobile device for processing information data. Certain requirements are imposed to perform a skin work operation: Appl. Sci. 2021, 11, 4737 8 of 24

• Conditions on the mobile device side are required, that is, a number of conditions, constraints, and criteria are imposed on the choice of devices to perform and assign work operations. We denote all these requirements by the following: V = V1, V2,... Vi,..., Vg , (8)

where g is the total number of requirements and conditions, the value of which is determined by the task setter; Vi is the i-th requirement determined by the operator. • A number of requirements are imposed on the quality of functioning of a work operation: W = {W1, W2,... Wh}, (9) where h is the number of requirements and performance criteria (support for the process of performing and the result of performing a work operation). The value of h (the number of requirements and criteria) is determined by the problem setter. Wi are the requirements for the execution of a work operation: responsiveness, minimum cost, and so on. Let us formulate a general statement of the optimization problem of efficient, liquid- free communication. At time t (tn), the state of the space environment Ω is as follows (i.e., the situation is decision-making): S(Ω) = {CV, D, Ro, V, W}. (10) where CV is the wireless communication station. Then at time tn it is necessary to choose such a wireless communication station CVi ∈ CV that satisfies the requirements of the function presented below. Q = F(W) = F W1, W2,... Wj → opt, (11) where W = W1, W2,... Wj , j < h, further by expression (11), we turn the function into a function of the following form: f (bij, rij,Pi). (12) Function (12) is the criterion to be maximized (throughput, QoS, transmission rate, relative to cost or a certain complex criterion). Each network has a finite radio resource Pi. min When mobile device i is allocated to network j, it uses its resource rij; rij is the minimum resource needed to satisfy the QoS requirements of the user, bij is a binary variable that is equal to 1 if mobile device i is allocated to network j, and otherwise is equal to 0. If all resources of the network are busy and new requests are received, the allocation of resources between users is carried out according to a specific policy, which is represented by the function pij(vj,Pj,Dj). It can depend on the total network capacity (vj), the number of mobile devices (Dj) and the vector of QoS requirements for all connections (Pj). Based on the above, we formulate a general statement of the optimum network selection problem for horizontal–vertical handover as an objective function: max f bij, rij,Pi . (13)

Thus, instead of solving problem (11), we solve problem (13), assuming that there ∑ ∑ bij = 1 is a mobile device i in the network. i=1, n j=1,m

bij ∈ {0.1} where i = 1, 2, . . . , n; j = 1, 2, . . . , m; (14)

 min  rij , where ∑ ∑ rij·bij ≤ Pj  i=1, n j=1,m rij = (15)  pij vj, Pj, Dj , where ∑ ∑ rij·bij > Pj  i=1, n j=1,m Appl. Sci. 2021, 11, x FOR PEER REVIEW 9 of 26

 min ⋅ ≤ rwhererbPij,  ij ij j  i,==11爊j,m = rij  牋 (15) Appl. Sci. 2021, 11, 4737 ()牋燚 ⋅ > 9 of 24  pv,ij jP, j j,where r ij b ij P j  i,==11爊j,m To solve this target function, we proposed an algorithm for optimizing a heterogeneous networkTo solve thisusing target big function,data technology, we proposed which an is algorithm shown below. for optimizing The proposed a heterogeneous scheme al- networklows the usingoptimization big data of technology, the infrastructure which is to shown use both below. user The and proposed network schemedata, which, allows in theturn, optimization will increase of the the efficiency infrastructure of the to netw useork both as user a whole. and network The structure data, which,of the network in turn, willrequired increase for thethe efﬁciencyalgorithm, of in the simplified network asform, a whole. is shown The structurein Figure of2. the network required for the algorithm, in simpliﬁed form, is shown in Figure2.

Figure 2. Simplified architecture of a proposed heterogeneous mobile network. Figure 2. Simplified architecture of a proposed heterogeneous mobile network. A heterogeneous network usually consists of many cells of different technologies. SuchA a multi-layeredheterogeneous network network architecture usually consists can provide of many high capacity,cells of different provide thetechnologies. necessary levelSuch ofa qualitymulti-layered of service. network By using architecture BD and adapting can provide different high networkcapacity, resources provide accordingthe neces- tosary dynamically level of quality changing of service. time characteristics, By using BD it isand possible adapting to improve different the network throughput resources of the wholeaccording network. to dynamically In order to improvechanging the time efficiency characteristics, of the network it is possible infrastructure to improve under thethe increasingthroughput load of the on whole the MNOs, network. it is recommendedIn order to improve to classify the efficiency the user traffic of the requestsnetwork to infra- the necessarystructure under network the resources increasing and load improve on the theMNOs, efficiency it is recommended of their distribution to classify through the user the usetraffic of intelligentrequests to and the analyticalnecessary informationnetwork resources based on and big improve data. Figure the efficiency3 shows theof their general dis- Appl. Sci. 2021, 11, x FOR PEER REVIEW 10 of 26 principletribution ofthrough the proposed the use algorithm of intelligent for the and optimization analytical information of a heterogeneous based on network, big data. using Fig- bigure data.3 shows the general principle of the proposed algorithm for the optimization of a heterogeneous network, using big data.

FigureFigure 3.3. StagesStages ofof thethe heterogeneousheterogeneous networknetwork self-optimizationself-optimization process.process.

The first stage is the collection of big data. Data collection can be achieved through user equipment (UE), radio access network (RAN) and Internet Service Providers (ISP). Events occurring in the UE are collected either through user programs or through a control signal. In a radio access network RAN with eNodeB (eNB), instantaneous data measurement reports of the QoS requirements from different users, a more detailed principle of collection, are given in the paper [21]. MNOs have a huge amount of data related to media/user services in a heterogeneous network. In addition, a large storage infrastructure must have scalable capacity as well as scalable performance. Thus, latency management must be simple and efficient in order to easily store and sort big data. The second stage is to analyze the collected data. After collecting and storing the data, another major challenge for MNOs is processing such huge amounts of data. The collected data are reusable, heterogeneous, in real-time and voluminous. For this reason, data analysis and information extraction technology are needed to process the data and transform them to optimize the network. So, this information can be used to develop adaptive resource management schemes. Data analysis allows MNOs to systematically manage different access networks and provide services to customers. BD network optimization functions are able to analyze big data to identify problems and decide what and how to optimize at the appropriate level of the heterogeneous network. Improvement measures based on optimization results are then implemented, using control functions in the RAN. In addition, optimization at the user level can be performed. In particular, for users who are in the same cell, optimization can be configured for each user based on the class of service (priority but not priority users). In addition, the BD network optimization function is able to predict traffic fluctuations, both in the local area and in the global coverage area, ultimately helping to improve network performance and quality of service for users. The third stage is the management of the radio access network operator’s resources. MNOs need to be informed about their long-term network deployment goals in terms of bandwidth, coverage, number and location of base stations, etc. They also require new resource allocation strategies to meet different traffic requirements across the coverage area. Thus, the use of big data analytics can be a new way to solve these problems. Net- work analytics involve monitoring and analyzing user statistics that enable the real-time prediction of critical points and the state of mobile networks. Based on the obtained MNOs data, intelligent decisions are made to serve users by balancing the load and prioritizing traffic to improve the efficiency of operation and provide the necessary quality of service in a heterogeneous network.

Appl. Sci. 2021, 11, 4737 10 of 24

The first stage is the collection of big data. Data collection can be achieved through user equipment (UE), radio access network (RAN) and Internet Service Providers (ISP). Events occurring in the UE are collected either through user programs or through a control signal. In a radio access network RAN with eNodeB (eNB), instantaneous data measurement reports of the QoS requirements from different users, a more detailed principle of collection, are given in the paper [21]. MNOs have a huge amount of data related to media/user services in a heterogeneous network. In addition, a large storage infrastructure must have scalable capacity as well as scalable performance. Thus, latency management must be simple and efficient in order to easily store and sort big data. The second stage is to analyze the collected data. After collecting and storing the data, another major challenge for MNOs is processing such huge amounts of data. The collected data are reusable, heterogeneous, in real-time and voluminous. For this reason, data analysis and information extraction technology are needed to process the data and transform them to optimize the network. So, this information can be used to develop adaptive resource management schemes. Data analysis allows MNOs to systematically manage different access networks and provide services to customers. BD network optimization functions are able to analyze big data to identify problems and decide what and how to optimize at the appropriate level of the heterogeneous network. Improvement measures based on optimization results are then implemented, using control functions in the RAN. In addition, optimization at the user level can be performed. In particular, for users who are in the same cell, optimization can be configured for each user based on the class of service (priority but not priority users). In addition, the BD network optimization function is able to predict traffic fluctuations, both in the local area and in the global coverage area, ultimately helping to improve network performance and quality of service for users. The third stage is the management of the radio access network operator’s resources. MNOs need to be informed about their long-term network deployment goals in terms of bandwidth, coverage, number and location of base stations, etc. They also require new resource allocation strategies to meet different traffic requirements across the coverage area. Thus, the use of big data analytics can be a new way to solve these problems. Network analytics involve monitoring and analyzing user statistics that enable the real- time prediction of critical points and the state of mobile networks. Based on the obtained MNOs data, intelligent decisions are made to serve users by balancing the load and prioritizing traffic to improve the efficiency of operation and provide the necessary quality of service in a heterogeneous network. At the fourth stage, the problem of heterogeneous network optimization is solved by applying a comprehensive method, which includes the procedure of vertical handover initiation, redistribution of flows and rejection of non-priority user sessions.

3.3. A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in HWN Using BD Analytics On the basis of the formed stages of the method of increasing the performance of the heterogeneous network, a model of the heterogeneous network environment is proposed which, in contrast to the known, uses the method of processing a large amount of data to monitor the processes of information transfer, analysis of tasks and output of necessary reports on switching or initiating the handover and allows to investigate the optimization of network infrastructure operator network by implementing an algorithm for the redistribution of its network resources and balancing. To simplify understanding, we proposed a hierarchical representation of the input data in the implementation of the developed complex heterogeneous network optimization process. In Figure4, the hierarchical structure of the input data for the modeling of the inves- tigated network consisting of 2G/3G/4G/5G/Wi-Fi technology, service and QoS planes is presented. Appl. Sci. 2021, 11, x FOR PEER REVIEW 11 of 26

At the fourth stage, the problem of heterogeneous network optimization is solved by applying a comprehensive method, which includes the procedure of vertical handover initiation, redistribution of flows and rejection of non-priority user sessions.

3.3. A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in HWN using BD Analytics On the basis of the formed stages of the method of increasing the performance of the heterogeneous network, a model of the heterogeneous network environment is proposed which, in contrast to the known, uses the method of processing a large amount of data to monitor the processes of information transfer, analysis of tasks and output of necessary reports on switching or initiating the handover and allows to investigate the optimization of network infrastructure operator network by implementing an algorithm for the redistribution of its network resources and balancing. To simplify understanding, we proposed a hierarchical representation of the input data in the implementation of the developed complex heterogeneous network optimization process. Appl. Sci. 2021, 11, 4737 In Figure 4, the hierarchical structure of the input data for the modeling of the inves-11 of 24 tigated network consisting of 2G/3G/4G/5G/Wi-Fi technology, service and QoS planes is presented.

FigureFigure 4. 4. HierarchicalHierarchical structure structure of of input input data data of of the the studied studied heterogeneous heterogeneous network.

TheThe technology planeplane includesincludes mobile mobile technologies technologies of theof the 2G, 2G, 3G, 3G, 4G, 5G4G, Cloud-RAN, 5G Cloud- RAN,and Wi-Fi and Wi-Fi network. network. For modeling, For modeling, it is assumedit is assumed that that all technologies all technologies operate operate within within the therange range of theof the cell cell 2G. 2G. Additionally, Additionally, only only one one base base station station that that worksworks withwith the presented technologiestechnologies is is considered considered in in the the paper. paper. Each Each of of the the base base stations receives receives requests from thethe UE, UE, which which are are divided divided into into priority priority requests requests (shown (shown in in Figure Figure 44,, markedmarked inin red)red) andand non-prioritynon-priority (marked inin blue).blue). For For example, example, it wasit was assumed assumed that that users users may may receive receive requests re- questsfor the for following the following types oftypes services: of services: •• CallsCalls as as voice voice transmission transmission over over IP IP networks networks (Voice (Voice over IP); • Digital Interactive Television (IPTV); • Digital Interactive Television (IPTV); • Internet data (I), which includes uploading and downloading data from internet • Internetresources; data (I), which includes uploading and downloading data from internet re- • sources;Video conferencing (Conf); •• VideoWEB. conferencing (Conf); • WEB.These types of services form the plane of services. Each base station receives requests fromThese the UE types with of theservices type ofform services, the plane respectively; of services. requests Each base from station users receives can form requests active fromand inactivethe UE sessions.with the Totype connect of services, and maintain respectively; the UE, requests the required from qualityusers can of serviceform active must andbe provided, inactive sessions. which is representedTo connect and in the maintain QoS plane. the UE, the required quality of service must Thebe provided, QoS plane which uses acceptableis represented session in the service QoS qualityplane. parameters according to ITU-T recommendationsThe QoS plane to uses ensure acceptable guaranteed session quality service for quality users. parameters according to ITU- T• recommendationsThroughputs (C); to ensure guaranteed quality for users. • • ThroughputsProbability of (C); losses (Pv); • Delay time (T); • Jitter (dt). For the practical implementation of the proposed big data approaches, it was decided to apply a cloud solution, namely, renting DigitalOcean cloud based on creating an account in DigitalOcean with setting up a server with Ubuntu 16.04 and installing the software needed for experimental research. The Cassandra database and the scalable Apache Spark data analysis platform were selected for the heterogeneous network, which is critical to the processing time [36]. Apache Cassandra is a scalable, fault-tolerant NoSQL database that is suitable for fast writing and reading of large amounts of unstructured data. Apache Spark is a fast and common engine for large-scale data processing. The general scheme of the heterogeneous network model using BD is depicted in Figure5. This is followed by the import of statistics into the Cassandra database, obtained by computer simulation, using the Makaroo utility (Figure6)[ 37]. The results of the generated heterogeneous network statistics is depicted in Figure7. Appl. Sci. 2021, 11, x FOR PEER REVIEW 12 of 26

• Probability of losses (Pv); • Delay time (T); • Jitter (dt). For the practical implementation of the proposed big data approaches, it was decided to apply a cloud solution, namely, renting DigitalOcean cloud based on creating an account in DigitalOcean with setting up a server with Ubuntu 16.04 and installing the software needed for experimental research. The Cassandra database and the scalable Apache Spark data analysis platform were selected for the heterogeneous network, which is critical to the processing time [36]. Apache Cassandra is a scalable, fault-tolerant NoSQL da- Appl. Sci. 2021, 11, 4737 tabase that is suitable for fast writing and reading of large amounts of unstructured data.12 of 24 Apache Spark is a fast and common engine for large-scale data processing. The general scheme of the heterogeneous network model using BD is depicted in Figure 5.

Appl. Sci. 2021, 11, x FOR PEER REVIEW 13 of 26

FigureFigure 5. General 5. General scheme scheme of ofthe the heterogeneous heterogeneous network network model model using using BD. BD.

This is followed by the import of statistics into the Cassandra database, obtained by computer simulation, using the Makaroo utility (Figure 6) [37]. The results of the generated heterogeneous network statistics is depicted in Figure 7.

Figure 6. Generated heterogeneous network statistics, using the Makaroo utility. Figure 6. Generated heterogeneous network statistics, using the Makaroo utility.

(a) (b) Figure 7. The results of generated heterogeneous network statistics: (a) The active_user table is filled; (b) the request_user table is full.

To calculate and analyze data, Jobs scripts are written in the Python programming language for Spark. When working, job-1 Spark performs a request to read data from the database Cassandra and processes them, calculating the number of active sessions on the network. The result is presented in Figure 8a. During operation, job-4 Spark simulates an active heavy load on the network. The result is presented in Figure 8b.

Appl. Sci. 2021, 11, x FOR PEER REVIEW 13 of 26

Appl. Sci. 2021, 11, 4737 13 of 24

Figure 6. Generated heterogeneous network statistics, using the Makaroo utility.

(a) (b)

FigureFigure 7. The 7. The results results of generated of generated heterogeneous heterogeneous network network statistics: statistics: (a) The(a) The active_user active_user table table is ﬁlled; is filled; (b) the(b) request_userthe request_user tabletable is full. is full.

To calculateTo calculate and and analyze analyze data, data, Jobs Jobs scripts scripts are are written written in the in the Python Python programming programming languagelanguage for for Spark. Spark. When When working, working, job-1 job-1 Spark Spark performs performs a request a request to read to read data data from from the the databasedatabase Cassandra Cassandra and and processes processes them, them, calculating calculating the the number number of active of active sessions sessions on theon the Appl. Sci. 2021, 11, x FOR PEER REVIEW 14 of 26 network.network. The The result result is presented is presented in Figure in Figure8a. During8a. During operation, operation, job-4 job-4 Spark Spark simulates simulates an an activeactive heavy heavy load load on theon the network. network. The The result result is presented is presented in Figure in Figure8b. 8b.

(a) (b)

Figure 8. ExperimentalExperimental results of developed big data analysis mobile system: ( aa)) Derivation Derivation of of the the maximum maximum available available and and current resource of the heterogeneous network;network; ((b)) activeactive sessionssessions duringduring peakpeak hours.hours.

In order to fully run the model and quickly process the statistical data, it is necessary to ensure that the Cassandra database interacts with the Spark platform. Next, we proceed to data analysis. Figure 9 shows the algorithm, which allows optimal management of heterogeneous network resources. The operation of the algorithm be- gins by writing input data to the Cassandra database. The input data include service requests from UEs as well as data on the state of the heterogeneous network (active sessions for each access technology). After recording the statistical data, we proceed to their analysis and comparison with the maximum allowable values for each of the technologies. With the help of data analysis, the critical points in the network are evaluated and decisions are made on the connection of requests from UEs (if there are free resources in the network) arriving at a particular point in time. If a heterogeneous network is loaded, a detailed NP (Network Parameters) analysis of active sessions and requests received is performed. Then, the free resources in the heterogeneous network are calculated and compared with the required number of resources to serve incoming requests. If the required number of resources exists, then the load is redistributed and balanced in the heterogeneous network, and signaling data are sent to each of the UEs with the optimal BS that can serve them. Otherwise, the priority of active sessions and incoming requests is analyzed. The non-priority sessions and requests are rejected and will be served later, while the priority requests are served with the required quality of service. After that, the algorithm is executed again after a time Δt. This algorithm can actually prove that mobile communication improves the transmission success rate, so that the user can continue driving while traveling without the mobile service being interrupted.

Appl. Sci. 2021, 11, 4737 14 of 24

Figure 9.Figure Algorithm 9. Algorithm of heterogeneous of heterogeneous network networksimulation simulation model using model BD using analysis. BD analysis.

IfWe a heterogeneoususe the developed network mathematical is loaded, meth a detailedod to generate NP (Network the data Parameters) analysis. analysis of active sessions and requestsP received (t)=− is P performed. (t) P Then, (t), the free resources in the heterogeneous network are calculatedareq.ij and compared max.ij with cur.ij the required number of resources = (16) to serve incoming requests. Pmax .ij const. If the required number of resources exists, then the load is redistributed and balanced where Pareq.ij (t) is the number of requests from users, which can be served by the BS at a in the heterogeneous network, and signaling data are sent to each of the UEs with the certain point in time, Pmax.ij (t) is the maximum allowable number of active sessions of users optimal BS that can serve them. Otherwise, the priority of active sessions and incoming without degradation of QoS, where i is the type of technology and j is the type of service, requests is analyzed. The non-priority sessions and requests are rejected and will be served and Pcur.ij (t) is the current number of active sessions at time t. later, while the priority requests are served with the required quality of service. After =+ that, the algorithm is executed againPinput.ij after(t)P(t)P(t), a req.ij time ∆t. Thisсur.ij algorithm can actually prove(17)

where Pinput.ij (t) is the number of incoming requests from users to the BS at a certain moment of time t. =− Plost .ij (t) P req.ij (t) P areq.ij (t), (18)

where Plost.ij (t) is the number of rejected requests from users, for a particular type of service at the time t. Based on the data obtained from the network analysis, graphs of heterogeneous network load were plotted. The analysis was performed for each technology and service separately to calculate and analyze user data Jobs scripts in the Python programming language for Spark. As an example, the results of the data analysis for overloaded 3G technology are shown in Figure 10.

Appl. Sci. 2021, 11, 4737 15 of 24

that mobile communication improves the transmission success rate, so that the user can continue driving while traveling without the mobile service being interrupted. We use the developed mathematical method to generate the data analysis.

P (t) = P (t) − P (t), areq.ij max.ij cur.ij (16) Pmax.ij = const.

where Pareq.ij (t) is the number of requests from users, which can be served by the BS at a certain point in time, Pmax.ij (t) is the maximum allowable number of active sessions of users without degradation of QoS, where i is the type of technology and j is the type of service, and Pcur.ij (t) is the current number of active sessions at time t.

Pinput.ij(t) = Preq.ij(t) + Pcur.ij(t), (17)

where Pinput.ij (t) is the number of incoming requests from users to the BS at a certain moment of time t. Plost.ij(t) = Preq.ij(t) − Pareq.ij(t), (18)

where Plost.ij (t) is the number of rejected requests from users, for a particular type of service at the time t. Based on the data obtained from the network analysis, graphs of heterogeneous network load were plotted. The analysis was performed for each technology and service Appl. Sci. 2021, 11, x FOR PEER REVIEWseparately to calculate and analyze user data Jobs scripts in the Python programming16 of 26 language for Spark. As an example, the results of the data analysis for overloaded 3G technology are shown in Figure 10.

Preq Pcur Pinput Pmax Preq Pcur Pinput Pmax 600 120 500 100 400 80 300 60 200 40 100 20 0 0 P(Preq, Pcur, Pinput,Pmax) P(Preq, Pcur, Pinput, Pmax) Pinput, Pcur, P(Preq, 12:00:00 12:00:08 12:00:16 12:00:24 12:00:32 12:00:40 12:00:48 12:00:56 12:01:04 12:01:12 12:01:20 12:01:28 12:01:36 12:01:44 12:01:52 12:02:00 12:02:08 12:02:16 12:02:24 12:02:32 12:02:40 12:02:48 12:02:56 12:03:04 12:03:12 12:00:00 12:00:07 12:00:14 12:00:21 12:00:28 12:00:35 12:00:42 12:00:49 12:00:56 12:01:03 12:01:10 12:01:17 12:01:24 12:01:31 12:01:38 12:01:45 12:01:52 12:01:59 12:02:06 12:02:13 12:02:20 12:02:27 12:02:34 12:02:41 12:02:48 12:02:55 12:03:02 12:03:09 12:03:16 Time Time (a) (b)

Preq Pcur Pinput Pmax Preq Pcur Pinput Pmax 60 60 40 40 20 20 Pmax) 0 Pmax) 0 P (Preq, Pcur, Pinput, P (Preq, P Pcur, Pinput,(Preq, 12:00:00 12:00:07 12:00:14 12:00:21 12:00:28 12:00:35 12:00:42 12:00:49 12:00:56 12:01:03 12:01:10 12:01:17 12:01:24 12:01:31 12:01:38 12:01:45 12:01:52 12:01:59 12:02:06 12:02:13 12:02:20 12:02:27 12:02:34 12:02:41 12:02:48 12:02:55 12:03:02 12:03:09 12:03:16 12:00:00 12:00:08 12:00:16 12:00:24 12:00:32 12:00:40 12:00:48 12:00:56 12:01:04 12:01:12 12:01:20 12:01:28 12:01:36 12:01:44 12:01:52 12:02:00 12:02:08 12:02:16 12:02:24 12:02:32 12:02:40 12:02:48 12:02:56 12:03:04 12:03:12 Time Time (c) (d)

Figure 10.10. ExperimentalExperimental resultsresults of of developed developed big big data data analysis analysis 3G 3G mobile mobile system: system: (a )(a The) The incoming incoming VoIP VoIP load load on 3G;on 3G; (b) the(b) incomingthe incoming internet internet downloading downloading on 3G; on (3G;c) the (c) incomingthe incoming internet internet uploading uploading load load on 3G; on (3G;d) the(d) incomingthe incoming IPTV IPTV load load on 3G; on (3G;e) the (e) incomingthe incoming web web load load on 3G; on (3G;f) the (f) incoming the incoming videoconference videoconference load load on 3G. on 3G.

In the work, for flexible resource management, it is proposed to estimate the system throughput, because as the number of users increases Hinput.ij, the available throughput Cfree decreases, which in turn leads to an increase in the service delay time and the growth of lost data, as shown in Figure 11.

↑ ↓ ↑↑ Hinput.ij (t) С free ,TP ,

С Т Р

Figure 11. Influence of the parameter of the current system throughput utilization on latency and losses of data.

For the optimal resource management, a comprehensive calculation method is proposed in this paper, which allows an even distribution of input load in a heterogeneous network. To determine the maximum throughput capacity of a heterogeneous network, we use the following formula: 5 = CC,max max.ij (19) n=1

where Cmax is the maximum heterogeneous network throughput and Cmax.ij is the maximum throughput of i-th technology of j-th service, n is the number of technologies in the heterogeneous network.

Appl. Sci. 2021, 11, x FOR PEER REVIEW 16 of 26

Preq Pcur Pinput Pmax Preq Pcur Pinput Pmax 70 140 60 120 50 100 40 80 30 60 20 40 10 20 0 0 P (Preq, Pcur, Pinput, Pmax) Pinput, Pcur, (Preq, P 12:00:00 12:00:08 12:00:16 12:00:24 12:00:32 12:00:40 12:00:48 12:00:56 12:01:04 12:01:12 12:01:20 12:01:28 12:01:36 12:01:44 12:01:52 12:02:00 12:02:08 12:02:16 12:02:24 12:02:32 12:02:40 12:02:48 12:02:56 12:03:04 12:03:12 12:00:00 12:00:07 12:00:14 12:00:21 12:00:28 12:00:35 12:00:42 12:00:49 12:00:56 12:01:03 12:01:10 12:01:17 12:01:24 12:01:31 12:01:38 12:01:45 12:01:52 12:01:59 12:02:06 12:02:13 12:02:20 12:02:27 12:02:34 12:02:41 12:02:48 12:02:55 12:03:02 12:03:09 12:03:16 Time P(Preq, Pcur, Pinput,Pmax) Time (e) (f) Appl. Sci.Figure2021 ,10.11, Experimental 4737 results of developed big data analysis 3G mobile system: (a) The incoming VoIP load on 3G; (b16) of 24 the incoming internet downloading on 3G; (c) the incoming internet uploading load on 3G; (d) the incoming IPTV load on 3G; (e) the incoming web load on 3G; (f) the incoming videoconference load on 3G.

InIn thethe work,work, forfor ﬂexibleflexible resourceresource management,management, itit isis proposedproposed toto estimateestimate thethe systemsystem throughput,throughput, because as as the the number number of of users users increases increases HHinput.ijinput.ij, the, theavailable available throughput throughput Cfree Cdecreases,free decreases, which which in turn in turnleads leads to an to increase an increase in the in theservice service delay delay time time and and the the growth growth of oflost lost data, data, as asshown shown in inFigure Figure 11. 11 .

↑ ↓ ↑↑ Hinput.ij (t) С free ,TP ,

С Т Р

Figure 11.11. InﬂuenceInfluence of the parameter of of the the current current sy systemstem throughput throughput utilization utilization on on latency latency and and losseslosses ofof data.data.

For thethe optimaloptimal resourceresource management,management, a comprehensivecomprehensive calculation method is pro- posedposed inin thisthis paper,paper, whichwhich allowsallows anan eveneven distributiondistribution ofof inputinput loadload inin aa heterogeneousheterogeneous network. ToTo determinedetermine the the maximum maximum throughput throughput capacity capacity of aof heterogeneous a heterogeneous network, network, we usewe use the followingthe following formula: formula: 5 5 Cmax = ∑ Cmax.ij, (19) =n= CC,max1 max.ij (19) = where Cmax is the maximum heterogeneous networkn 1 throughput and Cmax.ij is the maximum throughput of i-th technology of j-th service, n is the number of technologies in the where Cmax is the maximum heterogeneous network throughput and Cmax.ij is the maximum heterogeneousthroughput of i-th network. technology of j-th service, n is the number of technologies in the heterogeneous network. Cmax.ij = Pmax.ij·CQoS.j, (20)

where CQoS.j is the throughput needed to provide j-th service. Maximum allowable number of active user sessions for different technologies (Pmax.ij) is presented in the Table1 and services throughput requirements ( CQoS.j) are shown in Table2.

Table 1. Maximum allowable number of active user sessions for different technologies (Pmax.ij).

Technology/Service 2G 3G 4G Wi-Fi Cloud-RAN Call 1000 500 500 200 500 Conference 0 100 200 150 400 IPTV 0 50 100 150 200 Web 0 50 100 100 200 Internet (download) 0 50 100 150 200 Internet (upload) 0 100 200 250 400

Table 2. Services throughput requirements (CQoS.j).

Service Throughput (kbps) Call 64 Conference 2048 IPTV 4096 Web 10,240 Internet (download) 2048 Internet (upload) 2048

After calculating the maximum throughput of the network, proceed to the calculation of the throughput allocated to serve the current active sessions.

Ccur.ij = Pcur.ij·CQoS.j, (21) Appl. Sci. 2021, 11, 4737 17 of 24

where Ccur.ij is the throughput of the i-th technology of the j-th service, allocated for active sessions.

5 Ccur = ∑ Ccur.ij = Ccur.2G+Ccur.3G + Ccur.4G + Ccur.w + Ccur.c, (22) n=1

where Ccur is the total throughput, allocated by the heterogeneous network to serve the current load. For each of the technologies it is necessary to calculate the current throughput. Con- sider an example of calculating throughput for each of the technologies:

Ccur.2G = Pcur.call·CQoS.call, (23)

Ccur.3G = Pcur.3Gcall·CQoS.call + Pcur.3Gconf·CQoS.3Gconf + (Pcur.3Gid + Pcur.3Giup)·CQoS.i + Pcur.3Gweb·CQoS.web + Pcur.3Giptv·CQoS.iptv, (24)

Ccur.4G = Pcur.4Gcall·CQoS.call + Pcur.4Gconf·CQoS.4Gconf + (Pcur.4Gid + Pcur.4Giup)·CQoS.i + Pcur.4Gweb·CQoS.web + Pcur.4Giptv·CQoS.iptv, (25)

Ccur.c = Pcur.ccall·CQoS.call + Pcur.cconf·CQoS.4Gconf + (Pcur.cid + Pcur.ciup)·CQoS.i + Pcur.cweb·CQoS.web + Pcur.ciptv·CQoS.iptv, (26)

Ccur.w = Pcur.wcall·CQoS.call + Pcur.wconf·CQoS.4Gconf + (Pcur.wid + Pcur.wiup)·CQoS.i + Pcur.wweb·CQoS.web + Pcur.wiptv·CQoS.iptv, (27) We proceed to calculate the throughput to be allocated for requests coming from users.

6 Creq = ∑ Preq.j·CQoS.j, (28) j=1

where Creq is the throughput needed to serve incoming requests from users in a heterogeneous network. Closs = Cmax − (Creq + Ccur). (29)

where Closs is the throughput which is lost if the number of requests exceeds the maximum permissible values for theinﬂuence of the parameter of the current system throughput utilization on latency and losses of data.

4. Experimental Results In order to improve the efﬁciency of a heterogeneous network system, the paper proposes the implementation of a comprehensive method of optimization, including the following: • Dynamic reservation of the number of sessions, which allows to evenly distribute the load from users (proposed method 1); • Rejection of non-priority sessions to provide guaranteed quality of service to priority users (proposed method 2). First, let us consider the operation of a homogeneous network when mobile technologies work independently (traditional method 1) as described in paper [19]. The block diagram of the operation of each technology, separately, is depicted in Figure 12. Based on the analysis of homogeneous networks, histograms of the distribution of the number of active and lost requests for each available service show the state of each individual network. The total number of active and lost requests in homogeneous networks is depicted in Figure 13. Figure 12 shows that the base station with 2G and 3G technology are overloaded, while all other base stations of other mobile technologies are idle. Thus, the use of homogeneous networks is not optimal, because in the case of overloading of some technologies, requests from users are dropped, which leads to a degradation of the quality of service as well as a decrease in the network performance. Therefore, in this thesis it is proposed to use a heterogeneous network (traditional method 2), proposed by Appl. Sci. 2021, 11, x FOR PEER REVIEW 18 of 26

6 =⋅ CPC,req req. j QoS . j (28) j=1

where Creq is the throughput needed to serve incoming requests from users in a heterogeneous network. =− + CC(CC).loss max req cur (29)

where Closs is the throughput which is lost if the number of requests exceeds the maximum permissible values for theinfluence of the parameter of the current system throughput utilization on latency and losses of data.

4. Experimental Results In order to improve the efficiency of a heterogeneous network system, the paper proposes the implementation of a comprehensive method of optimization, including the following: • Dynamic reservation of the number of sessions, which allows to evenly distribute the load from users (proposed method 1); Appl. Sci. 2021, 11, 4737 • Rejection of non-priority sessions to provide guaranteed quality of service to priority18 of 24 users (proposed method 2). First, let us consider the operation of a homogeneous network when mobile technol- theogies authors work inindependently [17], to improve (traditional the quality method of service 1) as asdescribed well as toin reducepaper [19]. the number The block of dissatisﬁeddiagram of customers.the operation of each technology, separately, is depicted in Figure 12.

Appl. Sci. 2021, 11, x FOR PEER REVIEW 19 of 26

Figure 12.12. Block diagram of the operation ofof eacheach technologytechnology separatelyseparately (traditional(traditional methodmethod 1).1).

Based on the analysis of homogeneous networks, histograms of the distribution of 450 403 400 the number of active and lost requests for each available service show the state of each 350 individual network. The total number of active and lost requests in homogeneous net- 300 works is depicted in Figure 13. 250 200 145 150 100 100 84 82 82 8076 100 61 72646264 60 726472 72 72 72 58 50 5050 3646 48 50 0 2 8 0 2g 3g 4g cloud-ran wifi Loss

Call Conference IPTV Internetd Internetup web

Figure 13. The total number of active and lost requests in homogeneous networks (traditional method 1). Figure 13. The total number of active and lost requests in homogeneous networks (traditional method 1). With the increasing number of users and complexity of services, one communication networkFigure cannot 12 shows meet allthat QoS the requirements.base station with Heterogeneous 2G and 3G technology networks are, are therefore, overloaded, an effectivewhile all solution.other base Vertical stations handover of other (VHO) mobile is technologies an important are step idle. in the convergence process of heterogeneousThus, the use networks. of homogeneous An appropriate networks handover is not optimal, algorithm because can improve in the case the of quality over- ofloading service of (QoS)some oftechnologies, users. To solve requests the problem from users of network are dropped, congestion which caused leads to by a adegra- large numberdation of of the users quality connecting of service to partial as well networks as a decrease in heterogeneous in the network networks, performance. a load-balanced There- verticalfore, in this handover thesis algorithmit is proposed (Figure to use9) is a proposed.heterogeneous Among network the networks (traditional that method can meet 2), theproposed user requirements, by the authors the in [17], most to load-balanced improve the quality network of isservice obtained, as well using as to the reduce optimal the algorithm.number of dissatisfied In addition, customers. to take into account user requirements and ensure QoS of the networkWith service, the increasing we use number a process of ofusers analytical and complexity hierarchy of using services, big one data communication technology to weightnetwork different cannot networks.meet all QoS This requirements. method allows Heterogeneous the full use of differentnetworks network are, therefore, resources, an soeffective that the solution. load distribution Vertical handover between (VHO) networks is an is average.important step in the convergence process Weof heterogeneous proposed an approach networks. for An the approp studyriate of the handover processes algorithm of functioning can improve of the het-the erogeneousquality of service network (QoS) environment, of users. To which,solve th unlikee problem the knownof network ones, congestion uses the technique caused by of a processinglarge number large of users amounts connecting of data toto performpartial networks monitoring in heterogeneous of information networks, transfer, analysis a load- ofbalanced tasks that vertical are formed handover by network algorithm users (Figure and output 9) is proposed. of statistical Among data onthe the networks initiation that of handovercan meet the in theuser infrastructure requirements, of the mobile most communications. load-balanced network This approach is obtained, allowed using us the to optimal algorithm. In addition, to take into account user requirements and ensure QoS of the network service, we use a process of analytical hierarchy using big data technology to weight different networks. This method allows the full use of different network resources, so that the load distribution between networks is average. We proposed an approach for the study of the processes of functioning of the heterogeneous network environment, which, unlike the known ones, uses the technique of processing large amounts of data to perform monitoring of information transfer, analysis of tasks that are formed by network users and output of statistical data on the initiation of handover in the infrastructure of mobile communications. This approach allowed us to investigate the process of optimizing the operator’s network by implementing an algorithm to redistribute its network resources and provide flexible load balancing. The principle of operation of a heterogeneous network with static reservation of resources is presented in Figure 14.

Appl. Sci. 2021, 11, 4737 19 of 24

Appl. Sci. 2021, 11, x FOR PEER REVIEWinvestigate the process of optimizing the operator’s network by implementing an algorithm20 of 26

to redistribute its network resources and provide ﬂexible load balancing. Appl. Sci. 2021, 11, x FOR PEER REVIEW 20 of 26 The principle of operation of a heterogeneous network with static reservation of resources is presented in Figure 14. Heterogeneous network Requests from Heterogeneous network the UE Requests from the UE

Cmax.2G Cmax.3G Cmax.4G Cmax.w Cmax.c

Pmax.2 G Cmax.2G Cmax.3G Pmax.3G Cmax.4G Pmax.4G Cmax.w Pmax.w Cmax.c Pmax.c

Pmax.2 G max.c Ccur.2G Ccur.3G CPcur.4Gmax.3G CPcur.wmax.4G PCmaxcur.c.w P 2G 3G 4G Wi-Fi Cloud-Run Ccur.2G Ccur.3G Ccur.4G Ccur.w Ccur.c 2G 3G 4G Wi-Fi The lost Cloud-Run requests from UEThe lost requests from UE Figure 14. Block diagram of the heterogeneous network functioning process (traditional method 2). FigureFigure 14.14. Block diagramdiagram ofof thethe heterogeneousheterogeneous networknetwork functioningfunctioning processprocess(traditional (traditionalmethod method 2). 2)Figure. 14 shows the operation of a heterogeneous network; in this case, the total load coming Figurefrom users 14 shows is distributed the operation between of a the heterogeneous base stations network; according in to this the case, type the of service total load andcoming reservedFig fromure resource 1 users4 shows is on distributedthe the operation BS of each between of technology. a heterogeneous the base stations network according; in this to thecase type, the oftotal service load andcomingLet reserved us fromconsider resourceusers the is operationdistributed on the BS of ofbetween a each heteroge technology. theneous base stations network according in the case to ofthe an type incoming of service Let us consider the operation of a heterogeneous network in the case of an incoming load,and given reserved in the resource previous on example the BS offor each the settechnology. of homogeneous networks. The total num- load, given in the previous example for the set of homogeneous networks. The total number ber of activeLet us and consider lost requests the operation in hetero ofgenous a heterogeneous networks isnetwork depicted in inthe Figure case of 15. an incoming ofload, active given and in lost the requestsprevious in example heterogenous for the networksset of homogeneous is depicted networks. in Figure 15 The. total number of active and lost requests in heterogenous networks is depicted in Figure 15.

140 128 124 118 120 103 100 100 100 100100 100 78 80 60 60 40 38 33 40 25 27 20 6 5 4 0 0 0 0 2g 3g 4g cloud-run wifi Loss

Figure 15. The total number of active and lost requests in heterogenous networks (traditional method 2). Figure 15. The total numberCall of activeConference and lostIPTV requesInternetdts in heterogenousInternetup networksweb (traditional

method 2).As can be seen in Figure 16, the amount of load from users was redistributed. Redis- Figure 15. The total number of active and lost requests in heterogenous networks (traditional tribution occurred in accordance with the statistical reservation of the number of sessions method 2). allocatedAs can be to seen a particular in Figure type 16, of the service. amount Incoming of load from requests users were was redistributed redistributed. among Redis- the tributionBSs in occurred the heterogeneous in accordance network, with the the number statistical of reservation lost requests of decreased the number signiﬁcantly, of sessions and As can be seen in Figure 16, the amount of load from users was redistributed. Redis- allocatedthe performance to a particular of the type heterogeneous of service. In networkcoming requests increased. were redistributed among the tribution occurred in accordance with the statistical reservation of the number of sessions BSs in theA blockheterogeneous diagram ofnetwork, a heterogeneous the number network of lost withrequests load decreased balancing significantly, is depicted in allocated to a particular type of service. Incoming requests were redistributed among the andFigure the performance 16. of the heterogeneous network increased. BSsA blockin the diagram heterogeneous of a heterogeneous network, the network number with of lost load requests balancing decreased is depicted significantly in Fig- , ureand 16. the performance of the heterogeneous network increased. A block diagram of a heterogeneous network with load balancing is depicted in Fig- ure 16.

Appl. Sci. 2021, 11, x FOR PEER REVIEW 21 of 26 Appl. Sci. 2021, 11, 4737 20 of 24 Appl. Sci. 2021, 11, x FOR PEER REVIEW 21 of 26

Heterogeneous network Heterogeneous network Requests from Requeststhe UE from the UE

Cmax.2G Cmax.3G Cmax.w Cmax.c Cmax.4G

Cmax.2G Cmax.3G Cmax.w Cmax.c Cmax.4G Pmax.2G Pmax.3G Pmax.4G Pmax.w Pmax.c

Pmax.2G Pmax.3G Pmax.4G Pmax.w Pmax.c Ccur.2G Ccur.3G Ccur.4G Ccur.w Ccur.c

2G Ccur.3G 3G 4G cur.w Wi-Fi Ccur.c Ccur.2G Ccur.4G C Cloud-Run 2G 3G 4G Wi-Fi Cloud-Run The lost requestsThe lostfrom requestsUE from UE Figure 16. Block diagram of a heterogeneous network functioning process with load balancing (ourFigure proposed 16. Block method diagram 1). of a heterogeneous networknetwork functioningfunctioning processprocess withwith loadload balancingbalancing (our proposed(our proposed method method 1). 1). Figure 17 shows the resource allocation in a heterogeneous network, based on the dynamicFigure reservation 1717 showsshows of thethe number resourceresource of allocationallocation sessions for inin adifferenta heterogeneousheterogeneous types of network,services.network, The basedbased decision onon thethe ondynamic the required reservation number ofof thetheof sessions number forofof sessions each technology forfor different is made typestypes after ofof services.service estimatings. TheThe decisionthe re- sourceson the neededrequired required to numberserve number the of ofincoming sessions sessions loadfor for each at each ti metechnology technology t. This redistribution is ismade made after after improves estimating estimating the the per- there- formanceresourcessources neededof needed the heterogeneous to toserve serve the the incoming network, incoming load utilizes load at time at all time t.available This t. This redistribution resources redistribution and improves adapts improves the to theper- the incomingperformanceformance load. of the of theheterogeneous heterogeneous network, network, utilizes utilizes all available all available resources resources and and adapts adapts to the to theincoming incoming load. load.

450 403 400 350 300 250 200 151 131 124 150 100 100100 78 60 100 48 43 3825 27 50 5 4 0 0 0 0 0 0 0 2g 3g 4g cloud-run wifi Loss Call Conference IPTV Internetd Internetup web Figure 17. The total number of active and lost requests in proposed heterogenous networks with Figure 17. The total number of active and lost requests in proposed heterogenous networks with load balancing (our proposed method 1). loadFigure balancing 17. The (our total proposed number ofmethod active 1). and lost requests in proposed heterogenous networks with load balancingTo assess (our the optimization proposed method of the 1). network, the normalized value of the resources of each of theTo technologiesassess the optimization is taken into of account. the network, Performance the normalized evaluation value of the of proposed the resources integrated of eachmethod ofTo the (staticassess technologies andthe optimization dynamic is taken reservation) ofinto the acco network, forunt. heterogeneous Performance the normalized mobile evaluation value network of of the the using resources proposed big data of integratediseach depicted of the method intechnologies Figure (static 18. The andis taken KPI dynamic (Key into Performance account.reservation) Performance Indicator) for heterogeneous isevaluation a quantifiable mobile of the indicator networkproposed of usingtheintegrated results big data actuallymetho is depictedd achieved(static in and Figure by dynamic implementing 18. The reservation) KPI our(Key solutions. Performance for heterogeneous This In indicatordicator) mobile characterizesis a quanti-network fiabletheusing ratio indicator big between data of is thedepicted the results achieved in actually Figure result achieved18. and The the KPI by resources (Keyimplementing Performance utilized our by theIndicator)solutions. network isThis a from quanti- indi- the catormaximumfiable characterizes indicator available of the the to ratio results it (in between our actually work, the suchachieved achieved a resource by result implementing is theand maximum the resources our networksolutions. utilized capacity This by indi-the for networkacator different char fromacterizes wireless the maximum the technology). ratio availablebetween Network theto it achieved (in capacity our work, result is the such and amount athe resource resources of traffic is the thatutilized maximum a network by the networkcannetwork handle capacity from at any the for givenmaximum a different time. available This wireless includes to techno it (in the ourlogy). number work Network of, such maximum acapacity resource network is is the the amount throughput maximum of trafficornetwork maximum that capacitya network active for sessionscan a different handle as depictedat wireless any give in technology).n Figure time. 8This, highlighted includesNetwork the incapacity a number red box. is ofthe In maximum amount our work, of networktotraffic compare that throughput a our network solutions or can maximum withhandle the at known,active any given sessio we classifytime.ns as This depicted KPIs includes as K1,in Figure the K2, number K3, 8, where highlighted of coefficientmaximum in a K1networkred is box. an KPIInthroughput our indicator work, or to that maximumcompare determines our active solutions the sessions total with performance as thedepicted known, ofin homogeneous weFigure classify 8, highlighted KPIs networks as K1, in (traditionala red box. In method our work 1)., Theto compare coefficient our K2 solutions is an indicator with the that known, determines we classify the performance KPIs as K1,

Appl. Sci. 2021, 11, x FOR PEER REVIEW 22 of 26

Appl. Sci. 2021, 11, 4737 21 of 24

K2, K3, where coefficient K1 is an KPI indicator that determines the total performance of homogeneous networks (traditional method 1). The coefficient K2 is an indicator that de- Appl. Sci. 2021, 11, x FOR PEER REVIEW 22 of 26 oftermines a heterogeneous the performance network of a with heterogeneous static reservation network of with the numberstatic reservation of sessions of the for num- each typeber of of sessions service (traditionalfor each type method of service 2). The (traditional coefficient method K3 is an 2). indicator The coefficient that determines K3 is an theindicator performance that determines of a heterogeneous the performance network of a with heterogeneous dynamic reservation network with of the dynamic number res- of sessions for eachK2, K3 type, where of service coefficient (our K1 proposed is an KPI indicator method). that determines the total performance of ervation of thehomogeneous number of networks sessions (traditional for each typemethod of 1).service The coefficient (our proposed K2 is an method).indicator that determines the performance of a heterogeneous network with static reservation of the number of sessions for each type of service (traditional method 2). The coefficient K3 is an indicator that determines the performance of a heterogeneous network with dynamic reservation of the number of sessions for each type of service (our proposed method).

KPI

140% 120% 0% 100% 50% 22% 80% 60% 99% 86% 40% 70% 20% 0% K1 K2 K3 Figure 18. KPI evaluation of the proposedActive sessions integratedLost requests method with traditional methods for hetero- Figure 18. KPI evaluation of the proposed integrated method with traditional methods for heteroge- geneous mobile network using big data. neous mobile networkFigure 18. using KPI evaluation big data. of the proposed integrated method with traditional methods for heterogeneous mobile network using big data. FigureFigure 18 18 shows shows the the result result of of a a comprehensive comprehensive method method to to improve improve the the performance performance of aof heterogeneous a heterogeneous networkFigure network 18system, shows system, the which result whichallows of a comprehensiveallo anws overall an overall increase method increase to in improve network in network the performance performance perfor- ofmance 29%, of compared 29%,of compareda heterogeneo to the existing tous the network existing homogeneous system, homogeneous which systems. allows systems. an overall increase in network perfor- AA blockblock diagrammancediagram of 29%, of of a a compared heterogeneous heterogeneous to the existing network networ homogeneous functioningk functioning systems. process process with with load load balancing balanc- A block diagram of a heterogeneous network functioning process with load balanc- anding and user user prioritization prioritization is depicted is depicted in Figure in Figure 19. 19. ing and user prioritization is depicted in Figure 19.

HeterogeneousHeterogeneous network network RequestsRequests from from the UEthe UE

Cmax.2G Cmax.3G Cmax.4G Cmax.w Cmax.c

Cmax.2G Cmax.3G Pmax.2 Cmax.4G Pmax.3 Cmax.w Pmax.4 Cmax.c Pmax.w Pmax.c G G G Pmax.2 Pmax.3 Pmax.4 Pmax.w Pmax.c G G G Ccur.2G Ccur.3G Ccur.4G Ccur.w Ccur.c 2G 3G 4G Wi-Fi Cloud-Run Ccur.2G Ccur.3G Ccur.4G Ccur.w Ccur.c 2G 3G The4G lost Wi-Fi Cloud-Run requests from UE The lost requests from Figure 19. FigureBlock diagram 19. Block of a heterogeneous diagramUE of a network heterogeneous functioning network process with functioning load balancing process and withuser prioritization load balancing (our and proposed method 2). user prioritization (our proposed method 2). Figure 19 presents a block diagram of the distribution of incoming requests according Figure 19. Block diagram of a heterogeneousFigure 19 presents network afunctioning block diagram process of with the distribution load balancing of and incoming user prioritization requests according (our proposed method 2). to user priority. It is important for the operator to meet the needs of priority users. When to user priority.the Itinput is important load in a heterogeneous for the operator network to meetexceeds the the needs resources of priority of this network users. and When re- the input loadquests in a from heterogeneous users are rejected, network it is necessary exceeds to thetake resourcesinto account of the this priority network of requests and Figure 19 presents a block diagram of the distribution of incoming requests according requests from users are rejected, it is necessary to take into account the priority of requests toto reduceuser priority. the level It is of important user dissatisfaction. for the operat Accordingly,or to meet the the paper needs proposes of priority to users. analyze When the prioritythe input of load requests in a heterogeneous from the UE. In network this case, ex priorityceeds the requests resources will of go this to network active sessions, and re- andquests not from priority users to beare discarded. rejected, it If is the necessary network to resources take into are account not enough the priority to service of requests priority requests, then we analyze the active sessions and reject non-priority sessions.

Appl. Sci. 2021, 11, x FOR PEER REVIEW 23 of 26

Appl. Sci. 2021, 11, 4737 to reduce the level of user dissatisfaction. Accordingly, the paper proposes to analyze the 22 of 24 priority of requests from the UE. In this case, priority requests will go to active sessions, and not priority to be discarded. If the network resources are not enough to service priority requests, then we analyze the active sessions and reject non-priority sessions. FigureFigure 20 shows 20 shows a diagram a diagram of the network of the operation network with operation the analysis with of the the analysis priority of the priority of users.of users. In thisIn case, this the case, heterogeneous the heterogeneous network is maximally network isloaded maximally. At time loaded. t there are At time t there 100 arepriority 100 requests priority from requests users, from which users, can no which longer can be served. no longer In order be served. to allocate In orderre- to allocate sourcesresources for priority for priority users, we users, conduct we a conduct priority analysis a priority of the analysis current of sessions the current that are sessions that are non-priority but take up a large amount of resources. In this case, to serve 50 requests for non-priority but take up a large amount of resources. In this case, to serve 50 requests video conferencing and 50 requests for internet data, 15 current sessions (web) and 12 sessionsfor video(iptv) conferencingwere rejected. So, and neglecting 50 requests the forsmall internet number data, of non 15- currentpriority requests, sessions (web) and 12 guaranteedsessions bandwidth (iptv) were was rejected. provided So,to requests neglecting coming the from small priority number clients, of non-priority thereby requests, reducingguaranteed the number bandwidth of dissatisfied was users. provided to requests coming from priority clients, thereby reducing the number of dissatisﬁed users.

Priority requests (50 video K3 Lost priority sessions (15 web and 12 conferences, 50 Internet data) iptv)

Active sessions

Figure 20. Diagram of heterogeneous network functioning with estimation of user priority. Figure 20. Diagram of heterogeneous network functioning with estimation of user priority. 5. Conclusions 5. ConclusionWe analyzeds the existing problems in modern communication networks, such as the factWe analyzed that the the main existing problem problems of mobile in modern networks communication is the focus networks, on the such coverage as the area, not on fact thethat user,the main and problem the inability of mobile of the networks network is tothe adaptively focus on the respond coverage to area, bursts not ofon large amounts the user,of data and createdthe inability by of the the user. network Therefore, to adaptively the paperrespond proposes to bursts of to large develop amounts a heterogeneous of datanetwork, created whichby the user will. beTherefore, user-oriented the paper and proposes allow to the develop network a heterogeneous operator to analyze and network,predict which user will behavior be user-oriented through and the allow use ofthe cloud network technologies. operator to analyze and predict user behavior through the use of cloud technologies. In the work, a study of technologies for the effective functioning of heterogeneous In the work, a study of technologies for the effective functioning of heterogeneous mobilemobile networks networks was carried was carried out. It analyzes out. It analyzes the current the technology current technology and gives an and oppor- gives an opportu- tunitynity to improve to improve the quality the quality of service. of service. On the basis On theof the basis data of received the data as a received result of the as a result of the study,study, the model the model for improving for improving the performance the performance of a heterogeneous of a heterogeneous network using network a big using a big datadata processing processing system system was developed. was developed. The comparison The comparison of criteria for of effective criteria resource for effective resource managementmanagement was carried was carried out and out the andchoice the of choicethe criterion of the of criterionmaximum of uniform maximum loading uniform loading of theof mobile the mobile network network was made. was made. We developedWe developed a comprehensive a comprehensive method for method flexible for resource flexible management resource managementin a het- in a het- erogeneouserogeneous network, network, including including statistical statistical resource reservation resource reservation for a certain fortype a of certain service type of service in each technology, dynamic resource reservation, and user priority analysis, which can in each technology, dynamic resource reservation, and user priority analysis, which can reduce the number of dissatisfied customers. reduceWe proposed the number the use ofof dissatisfiedbig data technologies customers. for optimal resource management in mobile networks.We proposed For effective the use data of analysis big data in a technologies heterogeneous for system, optimal we classified resource the management in datamobile into two networks. types: user Fordata effective and network data operators analysis’ data. in a heterogeneousThe analysis of both system, types weof classified the datadata allows into us twoto isolate types: valuable user data information and network that is used operators’ for network data. optimization The analysis and of both types of flexibledata resource allows management. us to isolate valuable information that is used for network optimization and flexible resource management. As a practical result, we implemented a big data processing system. For its implementation, we used the cloud computing service DigitalOcean in which we created an account and configured a virtual server for data processing. We used the technology of virtualization at the operating system level with the help of the Docker platform, which allowed us to create two separate virtual containers, where we deployed non-relational database Apache Cassandra and the platform for fast real-time data analysis, Apache Spark. Scripts were developed in the Python programming language to analyze large amounts of data and, by filtering and sequencing, to output the correct data, allowing intelligent decisions to be made for resource management and predicting the behavior of a heterogeneous network. Appl. Sci. 2021, 11, 4737 23 of 24

We evaluated the effectiveness of the proposed solutions and achieved a 16% increase in performance of the heterogeneous network by using the statistical network resource reservation method, compared to homogeneous networks, and a 13% increase by using uniform resource allocation and a dynamic reservation process, compared to the previous method. Through user prioritization, the quality of user service in the heterogeneous network is improved and the number of dissatisﬁed customers is reduced.

Author Contributions: All authors contributed to the study conception and design. Methodology, M.B. and H.B. Formal analysis and investigation, O.Y. and H.B. Writing of the original draft prepa- ration N.K. and H.B. Writing of review and editing, M.B. and N.K. Funding acquisition N.K. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the Ukrainian project No. 0120U100674 “Development of the novel decentralized mobile network based on blockchain-architecture and artificial intelligence for 5G/6G development in Ukraine”. This work was supported by the Ukrainian project No. 0120U102201 “Development the methods and unified software-hardware means for the deployment of the energy efficient intent-based multi-purpose information and communication networks”, and by the Faculty of Management of Comenius University in Bratislava, Slovakia. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Bai, W.; Xiao, Y.; Hu, D.; Zhang, Y. Application Research of Multi-Mode Relay in Future Heterogeneous Networks. Appl. Sci. 2019, 9, 3934. [CrossRef] 2. Zhang, H.; Huang, C.; Zhou, J.; Chen, L. QoS-Aware Virtualization Resource Management Mechanism in 5G Backhaul Het- erogeneous Networks. IEEE Access 2020, 8, 19479–19489. [CrossRef] 3. Chettri, L.; Bera, R. A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems. IEEE Internet Things J. 2020, 7, 16–32. [CrossRef] 4. Chen, M.; Ullah, S.; Wang, L.; Chen, J.; Wei, X.; Kim, K.; Xu, J. Analysis and Scheduling in a 5G Heterogeneous Content De-livery Network. IEEE Access 2018, 6, 44803–44814. [CrossRef] 5. Beshley, M.; Kryvinska, N.; Seliuchenko, M.; Beshley, H.; Shakshuki, E.M.; Yasar, A.-U.-H. End-to-End QoS “Smart Queue” Management Algorithms and Traffic Prioritization Mechanisms for Narrow-Band Internet of Things Services in 4G/5G Net-works. Sensors 2020, 20, 2324. [CrossRef] 6. Asghari, M.; Yousefi, S.; Niyato, D. An Analysis of Service Bundles of Mobile Network Operators with Free Services Includ-ed. IEEE Trans. Mob. Comput. 2020, 19, 1789–1803. 7. Höyhtyä, M.; Lähetkangas, K.; Suomalainen, J.; Hoppari, M.; Kujanpää, K.; Ngo, K.T.; Kippola, T.; Heikkilä, M.; Posti, H.; Mäki, J.; et al. Critical Communications Over Mobile Operators’ Networks: 5G Use Cases Enabled by Licensed Spectrum Sharing, Network Slicing and QoS Control. IEEE Access 2018, 6, 73572–73582. [CrossRef] 8. Beshley, M.; Veselý, P.; Pryslupskyi, A.; Beshley, H.; Kyryk, M.; Romanchuk, V.; Kahalo, I. Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking. Appl. Sci. 2020, 10, 8223. [CrossRef] 9. Klymash, M.; Beshley, M.; Stryhaluk, B. System for Increasing Quality of Service of Multimedia Data in Convergent Net-works. In Proceedings of the 2014 First International Scientific-Practical Conference Problems of Infocommunications Science and Technology, Kharkov, Ukraine, 22 December 2014; pp. 63–66. 10. Luján, E.; Zuloaga Mellino, J.A.; Otero, A.D.; Vega, L.R.; Galarza, C.G.; Mocskos, E.E. Extreme Coverage in 5G Narrow-band IoT: A LUT-Based Strategy to Optimize Shared Channels. IEEE Internet Things J. 2020, 7, 2129–2136. [CrossRef] 11. Al-Shammari, B.K.J.; Al-Aboody, N.; Al-Raweshidy, H.S. IoT Traffic Management and Integration in the QoS Supported Network. IEEE Internet Things J. 2018, 5, 352–370. [CrossRef] 12. Demydov, I.; Seliuchenko, M.; Beshley, M.; Brych, M. Mobility Management and Vertical Handover Decision in an Always Best Connected Heterogeneous Network. In Proceedings of the Experience of Designing and Application of CAD Systems in Microelectronics, Lvi, Ukraine, 24–27 February 2015; pp. 103–105. 13. He, Y.; Yu, F.R.; Zhao, N.; Yin, H.; Yao, H.; Qiu, R.C. Big Data Analytics in Mobile Cellular Networks. IEEE Access 2016, 4, 1985–1996. [CrossRef] 14. Goudarzi, M. Heterogeneous Architectures for Big Data Batch Processing in MapReduce Paradigm. IEEE Trans. Big Data 2017, 5, 18–33. [CrossRef] Appl. Sci. 2021, 11, 4737 24 of 24

15. Zhang, L.; Shi, Y.; Chang, Y.-C.; Lin, C.-T. Hierarchical Fuzzy Neural Networks With Privacy Preservation for Heterogeneous Big Data. IEEE Trans. Fuzzy Syst. 2020, 29, 46–58. [CrossRef] 16. Ma, B.; Guo, W.; Zhang, J. A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning. IEEE Access 2020, 8, 35606–35637. [CrossRef] 17. Shao, G.; Wu, W.; Yin, L.; Ding, C. A Load Balancing Vertical Handoff Algorithm Considering QoS of Users for Heterogene-ous Networks in Power Communication. J. Phys. Conf. Ser. 2019, 1302, 022099. [CrossRef] 18. Su, D.; Wen, X.; Zhang, H.; Zheng, W. A Self-optimizing Mobility Management Scheme Based on Cell ID Information in High Velocity Environment. In Proceedings of the IEEE Second International Conference on Computer and Network Technology, Bangkok, Thailand, 23–25 April 2010; pp. 285–288. 19. Saeed, M.; Kamal, H.; El-Ghoneimy, M. Novel type-2 fuzzy logic technique for handover problems in a heterogeneous network. Eng. Optim. 2017, 50, 1533–1543. [CrossRef] 20. Alhabo, M.; Zhang, L. Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks. IEEE Access 2018, 6, 40547–40558. [CrossRef] 21. Martinez-Mosquera, D.; Navarrete, R.; Luján-Mora, S. Development and Evaluation of a Big Data Framework for Perfor-mance Management in Mobile Networks. IEEE Access 2020, 8, 226380–226396. [CrossRef] 22. Biswash, S.K.; Jayakody, D.N.K. A Fog Computing-Based Device-Driven Mobility Management Scheme for 5G Net-works. Sensors 2020, 20, 6017. [CrossRef] 23. Sultan, K.; Ali, H.; Zhang, Z. Big Data Perspective and Challenges in Next Generation Networks. Future Internet 2018, 10, 56.[CrossRef] 24. Qu, K.; Zhuang, W.; Ye, Q.; Shen, X.; Li, X.; Rao, J. Dynamic Flow Migration for Embedded Services in SDN/NFV-Enabled 5G Core Networks. IEEE Trans. Commun. 2020, 68, 2394–2408. [CrossRef] 25. Beshley, M.; Pryslupskyi, A.; Panchenko, O.; Beshley, H. SDN/Cloud Solutions for Intent-Based Networking. In Proceedings of the 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), Lviv, Ukraine, 2–6 July 2019; pp. 22–25. 26. Klymash, M.; Savchuk, R.; Pozdnyakov, P.; Beshley, M. The Researching and Modeling of Structures of Mobile Networks for Providing of Multiservice Radio Access. In Proceedings of the International Conference on Modern Problem of Radio Engi-neering, Telecommunications and Computer Science (TCSET), Lviv-Slavske, Ukraine, 21–24 February 2012; pp. 281–282. 27. Beshley, M.; Romanchuk, V.; Seliuchenko, M.; Masiuk, A. Investigation the Modified Priority Queuing Method Based on Vir- tualized Network Test Bed. In Proceedings of the Experience of Designing and Application of CAD Systems in Mi-croe-lectronics, Lviv, Ukraine, 24–27 February 2015; pp. 1–4. 28. Barona López, L.I.; Valdivieso Caraguay, Á.L.; Sotelo Monge, M.A.; García Villalba, L.J. Key Technologies in the Context of Future Networks: Operational and Management Requirements. Future Internet 2017, 9, 1. [CrossRef] 29. Fan, S.; Tian, H.; Wang, W. A Radio Resource Virtualization-Based RAT Selection Scheme in Heterogeneous Networks. IEEE Commun. Lett. 2017, 21, 1147–1150. [CrossRef] 30. El Helou, M.; Ibrahim, M.; Lahoud, S.; Khawam, K.; Mezher, D.; Cousin, B. A Network-Assisted Approach for RAT Selection in Heterogeneous Cellular Networks. IEEE J. Sel. Areas Commun. 2015, 33, 1055–1067. [CrossRef] 31. Habibi, M.A.; Nasimi, M.; Han, B.; Schotten, H.D. A Comprehensive Survey of RAN Architectures Toward 5G Mobile Communi- cation System. IEEE Access 2019, 7, 70371–70421. [CrossRef] 32. Bazzi, A.; Masini, B.M.; Zanella, A.; Dardari, D. Performance evaluation of softer vertical handovers in multiuser heterogeneous wireless networks. Wirel. Netw. 2017, 23, 159–176. [CrossRef] 33. Zheng, K.; Yang, Z.; Zhang, K.; Chatzimisios, P.; Yang, K.; Xiang, W. Big data-driven optimization for mobile networks toward 5G. IEEE Netw. 2016, 30, 44–51. [CrossRef] 34. Garcia-Pineda, M.; Felici-Castell, S.; Segura-Garcia, J. Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services. Netw. Protoc. Algorithms 2016, 8, 126–147. [CrossRef] 35. Ishteyaq, I.; Masoodi, I.S.; Muzaffar, K. Six-Element MIMO Antenna with Slot Ring Radiators for Future 5G Hand-Held Mo-bile Applications. In Proceedings of the 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), Vijiyapur, India, 8–10 October 2020; pp. 1–4. 36. Hadoop, Storm, Samza, Spark, and Flink: Big Data Frameworks Compared. Available online: https://www.digitalocean.com/ community/tutorials/hadoop-storm-samza-spark-and-flink-big-data-frameworks-compared (accessed on 18 March 2021). 37. Mockaroo—Random Data Generator and API Mocking Tool|JSON/CSV/SQL/Excel. Available online: https://www.mockaroo. com/ (accessed on 18 March 2021).